Modelling the sex-age distribution of the global refugee and asylum-seeker population

Outline of descriptive analysis

• Describe UNHCR official population statistics content and compilation
• Show and explore structure of data sets: especially asylum - origin - location.
• Show origin and asylum country structure in missing and non-missing demographic data
• Explain choice of age brackets (18-59 or finer?): show population and country coverage

Introduction

Data set

UNHCR compiles official statistics on stocks and flows of forcibly displaced and stateless persons twice a year, once for mid-year figures (Mid-Year Statistical Reporting, MYSR) and once for end-year figures (Annual Statistical Reporting, ASR). For these reporting exercises, country operations compile aggregate population figures from a range of sources and data producers such as governments, UNHCR’s own refugee registration database proGres and sometimes non-governmental actors. The figures undergo a statistical quality control process at the country, regional and global level of the organisation and are disseminated on the publicly available refugee data finder (https://www.unhcr.org/refugee-statistics/) after undergoing a light statistical disclosure control process to suppress very small counts of persons that could identify individuals.

The end-year figures compiled with reporting date 31 December contain sex- and age breakdowns of the stocks of displaced and stateless people under UNHCR’s mandate. Table @ref(tab:demref2020)) displays the sex- and age-disaggregated data on the stock of refugees under UNHCR’s mandate (including Venezuelans displaced abroad, excluding Palestine refugees under UNRWA’s mandate). The data is available on sub-national level as indicated by the variables location and urbanRural. Variable statelessStatus displays whether the reported population is stateless (STL and UDN) or not stateless (NSL). The variables [sex]_[agebracket] contain the counts of refugees as of 31 December 2020 in the individual sex and age brackets in the respective geographic/stateless combination. For example, female_12_17 contains the number of female refugees aged 12 to 17. Variable totalEndYear is the total number of refugees over all sex/age categories.

demref2020 %>% 
  select(asylum_country, origin_country, location, urbanRural, statelessStatus, female_0_4:totalEndYear, typeOfDisaggregation, typeOfDisaggregationBroad)

Descriptives of age distribution and missingness

Demographic disaggregation coverage by asylum region

Globally - % per age/sex cat. - % age missing - % age and sex missing

By CoO - % per age/sex cat. - % age missing - % age and sex missing

Demographic disaggregation coverage by origin country and asylum region

2020 end-year refugee/Venezuelan population by origin country and asylum region

By CoA - % per age/sex cat. - % age missing - % age and sex missing

Discuss types and reasons for missingness (NMAR) and outline modelling approach to overcome. Why is using available data so bad?